HiIndex: An Efficient Spatial Index for Rapid Visualization of Large-Scale Geographic Vector Data

In the big data era, rapid visualization of large-scale vector data has become a serious challenge in Geographic Information Science (GIS). To fill the gap, we propose HiIndex, a spatial index that enables real-time and interactive visualization of large-scale vector data. HiIndex improves the state of the art with its low memory requirements, fast construction speed, and high visualization efficiency. In HiIndex, we present a tile-quadtree structure (TQ-tree) which divides the global geographic range based on the quadtree recursion method, and each node in the TQ-tree represents a specific and regular spatial range. In this paper, we propose a quick TQ-tree generation algorithm and an efficient visualization algorithm. Experiments show that the HiIndex is simple in structure, fast in construction, and less in memory occupation, and our approach can support interactive and real-time visualization of billion scale vector data with negligible pre-treatment time.

VARIASI POHON FRAKTAL MENGGUNAKAN L-SYSTEMS

Fractal tree is simply a trunk and a number of branches, each of which looks like the tree itself. The fractal tree can be generated using the IFS and L-Systems methods. In this article, the author develops fractal tree generation using L-Systems with additional variations. The variations given are in thickness, length, and branch angle. This development is expected to produce more diverse fractal tree patterns. In generating a fractal tree using L-Systems, it begins by determining the letters and symbols to be used. Then determine which axioms should be used. Then the production rules are made together with the determination of the parametric L-Systems. And the last is to determine the probability value for the stochastic L-Systems. In the deterministic L-Systems, thickness variations, length variations, and branch angle variations are carried out. In the variation of branch thickness, if the ratio of the thickness of the left branch is greater than that of the right branch, the fractal tree is skewed to the left. Then in the variation of branch length if the ratio of the length of the left branch is smaller than the ratio of the length of the right branch, the length of the left branch is longer than the length of the right branch. Then at the angle of the branching the smaller the 𝜃 that is included causes the branches to be closer together. The use of stochastic L-Systems can produce more diverse fractal tree patterns, even though they use the same production rules and parameter values

Handling Complex Queries Using Query Trees

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.

Handling Complex Queries Using Query Trees

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.